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Linear Regression using Heterogeneous Data Batches
Ayush Jain, Rajat Sen, Weihao Kong +2
In many learning applications, data are collected from multiple sources, each providing a \emph{batch} of samples that by itself is insufficient to learn its input-output relations…
Efficient List-Decodable Regression using Batches
Abhimanyu Das, Ayush Jain, Weihao Kong +1
We begin the study of list-decodable linear regression using batches. In this setting only an fraction of the batches are genuine. Each genuine batch contains …
Robust estimation algorithms don't need to know the corruption level
Ayush Jain, Alon Orlitsky, Vaishakh Ravindrakumar
Real data are rarely pure. Hence the past half-century has seen great interest in robust estimation algorithms that perform well even when part of the data is corrupt. However, the…
Linear-Sample Learning of Low-Rank Distributions
Ayush Jain, Alon Orlitsky
Many latent-variable applications, including community detection, collaborative filtering, genomic analysis, and NLP, model data as generated by low-rank matrices. Yet despite cons…
Optimal Robust Learning of Discrete Distributions from Batches
Ayush Jain, Alon Orlitsky
Many applications, including natural language processing, sensor networks, collaborative filtering, and federated learning, call for estimating discrete distributions from data col…